Abbrevation
BDAA
City
Orleans
Country
France
Deadline Paper
Start Date
End Date
Abstract

The challenge of “Big Data” continues to grow and is an active area of significant research&#046; This track focuses on techniques, experiences, applications, and lessons learned for large&#8211;scale/big data science, data intensive, and data analytics, particularly in the realms of high performance computing that lead organizations through major transformations&#046;<br>The symposium is to address, explore and exchange information on the state&#8211;of&#8211;the&#8211;art and practice in the broad multidisciplinary field of Big Data Science&#046; Participation is extended to researchers, designers, educators and interested parties in all disciplines and specialties<br>The symposium aims at providing a forum to bring together researchers and scientists to share and exchange big data related research, technologies, experiences, and lessons for building various types large&#8211;scale data intensive and data analytics, with interoperability and coordination capabilities in a high performance and high availability setting&#046;<br>This symposium solicits contributions that address contemporary and future challenges in big data Science, data analytics, and Data Intensive, particularly in collaboration systems, social networks and media, and information technologies&#046;<br>BDAA topics include (but are not limited to) the following:<br>&#8211; Theories and Methodologies for Big Data processing<br>&#8211; Architectures and Design of Big Data processing systems<br>&#8211; Distributed data&#8211;intensive computing systems<br>&#8211; Managing large&#8211;scale big data platforms<br>&#8211; Use of big data technologies for science (Hadoop, NoSQL/NewSQL, etc&#046;)<br>&#8211; Big data simulation, visualization, modeling tools, and algorithms<br>&#8211; Big data intelligence and predictive analysis<br>&#8211; Discovery, Collection, and Extraction of information in Big Data sources<br>&#8211; Processing of Big Data Streaming and Time Series<br>&#8211; Big Data Mining and Knowledge Discovery<br>&#8211; Applications using big data (WEB, Bio&#8211;Data, Industrial Data, etc&#046;)<br>&#8211; Big data business implications – Data culture<br>&#8211; Experiences, Case Studies and Lessons Learned<br>